code: def u_hat(self, u, w):
      alpha = torch.matmul(u.unsqueeze(-2), w.unsqueeze(-1)).squeeze(-1)
      a_prime = -1 + F.softplus(alpha)
      return u + (a_prime - alpha) * w.div(
          w.pow(2).sum(dim=-1, keepdim=True)
      )
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 2 for calling the ConditionedRadial obj's underlying member method code with example usage: obj.u_hat(u, w) and returns: unknown